Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems

Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system mode...
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Autor*in:	Dong Guo [verfasserIn] Wei Wang [verfasserIn] Yuntao Zhang [verfasserIn] Qiuzhen Yan [verfasserIn] Jianping Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 142232-142238
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:142232-142238

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3119348

Katalog-ID:	DOAJ019706251

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allfields	10.1109/ACCESS.2021.3119348 doi (DE-627)DOAJ019706251 (DE-599)DOAJ8f5597288cd94cc5a1070347b16e40d0 DE-627 ger DE-627 rakwb eng TK1-9971 Dong Guo verfasserin aut Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme. Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering Wei Wang verfasserin aut Yuntao Zhang verfasserin aut Qiuzhen Yan verfasserin aut Jianping Cai verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 142232-142238 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:142232-142238 https://doi.org/10.1109/ACCESS.2021.3119348 kostenfrei https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 kostenfrei https://ieeexplore.ieee.org/document/9568908/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 142232-142238
spelling	10.1109/ACCESS.2021.3119348 doi (DE-627)DOAJ019706251 (DE-599)DOAJ8f5597288cd94cc5a1070347b16e40d0 DE-627 ger DE-627 rakwb eng TK1-9971 Dong Guo verfasserin aut Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme. Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering Wei Wang verfasserin aut Yuntao Zhang verfasserin aut Qiuzhen Yan verfasserin aut Jianping Cai verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 142232-142238 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:142232-142238 https://doi.org/10.1109/ACCESS.2021.3119348 kostenfrei https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 kostenfrei https://ieeexplore.ieee.org/document/9568908/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 142232-142238
allfields_unstemmed	10.1109/ACCESS.2021.3119348 doi (DE-627)DOAJ019706251 (DE-599)DOAJ8f5597288cd94cc5a1070347b16e40d0 DE-627 ger DE-627 rakwb eng TK1-9971 Dong Guo verfasserin aut Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme. Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering Wei Wang verfasserin aut Yuntao Zhang verfasserin aut Qiuzhen Yan verfasserin aut Jianping Cai verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 142232-142238 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:142232-142238 https://doi.org/10.1109/ACCESS.2021.3119348 kostenfrei https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 kostenfrei https://ieeexplore.ieee.org/document/9568908/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 142232-142238
allfieldsGer	10.1109/ACCESS.2021.3119348 doi (DE-627)DOAJ019706251 (DE-599)DOAJ8f5597288cd94cc5a1070347b16e40d0 DE-627 ger DE-627 rakwb eng TK1-9971 Dong Guo verfasserin aut Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme. Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering Wei Wang verfasserin aut Yuntao Zhang verfasserin aut Qiuzhen Yan verfasserin aut Jianping Cai verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 142232-142238 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:142232-142238 https://doi.org/10.1109/ACCESS.2021.3119348 kostenfrei https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 kostenfrei https://ieeexplore.ieee.org/document/9568908/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 142232-142238
allfieldsSound	10.1109/ACCESS.2021.3119348 doi (DE-627)DOAJ019706251 (DE-599)DOAJ8f5597288cd94cc5a1070347b16e40d0 DE-627 ger DE-627 rakwb eng TK1-9971 Dong Guo verfasserin aut Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme. Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering Wei Wang verfasserin aut Yuntao Zhang verfasserin aut Qiuzhen Yan verfasserin aut Jianping Cai verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 142232-142238 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:142232-142238 https://doi.org/10.1109/ACCESS.2021.3119348 kostenfrei https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 kostenfrei https://ieeexplore.ieee.org/document/9568908/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 142232-142238
language	English
source	In IEEE Access 9(2021), Seite 142232-142238 volume:9 year:2021 pages:142232-142238
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topic_facet	Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Dong Guo @@aut@@ Wei Wang @@aut@@ Yuntao Zhang @@aut@@ Qiuzhen Yan @@aut@@ Jianping Cai @@aut@@
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callnumber-first	T - Technology
author	Dong Guo
spellingShingle	Dong Guo misc TK1-9971 misc Pneumatic artificial muscle systems misc adaptive iterative learning control misc initial position problem misc alignment condition misc Lyapunov approach misc Electrical engineering. Electronics. Nuclear engineering Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems
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topic_title	TK1-9971 Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems Pneumatic artificial muscle systems adaptive iterative learning control initial position problem alignment condition Lyapunov approach
topic	misc TK1-9971 misc Pneumatic artificial muscle systems misc adaptive iterative learning control misc initial position problem misc alignment condition misc Lyapunov approach misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Pneumatic artificial muscle systems misc adaptive iterative learning control misc initial position problem misc alignment condition misc Lyapunov approach misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Pneumatic artificial muscle systems misc adaptive iterative learning control misc initial position problem misc alignment condition misc Lyapunov approach misc Electrical engineering. Electronics. Nuclear engineering
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title	Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems
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title_full	Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems
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title_sort	angle tracking robust learning control for pneumatic artificial muscle systems
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title_auth	Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems
abstract	Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme.
abstractGer	Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme.
abstract_unstemmed	Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme.
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title_short	Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems
url	https://doi.org/10.1109/ACCESS.2021.3119348 https://doaj.org/article/8f5597288cd94cc5a1070347b16e40d0 https://ieeexplore.ieee.org/document/9568908/ https://doaj.org/toc/2169-3536
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